| | ---
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| | language:
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| | - en
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| | license: mit
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| | library_name: openpeerllm
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| | pipeline_tag: text-generation
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| | tags:
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| | - pytorch
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| | - causal-lm
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| | - decentralized-learning
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| | - transformer
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| | - boinc
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| | - decent-torch
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| | - lonscript
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| | datasets:
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| | - custom
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| | model-index:
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| | - name: OpenPeerLLM
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| | results:
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| | - task:
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| | name: Language Modeling
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| | type: text-generation
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| | dataset:
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| | name: Custom Text Dataset
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| | type: text
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| | metrics:
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| | - name: Epoch
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| | type: number
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| | value: 2
|
| | - name: Model Size
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| | type: text
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| | value: "1.82 GB"
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| | - name: Run Time
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| | type: text
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| | value: "2.5 minutes on Intel UHD Graphics 630"
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| | - name: Loss
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| | type: cross-entropy
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| | value: 7.11
|
| | ---
|
| |
|
| | # OpenPeerLLM: A Decentralized Large Language Model
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| |
|
| | [](https://doi.org/10.57967/hf/6469)
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| |
|
| | This project implements a decentralized Large Language Model (LLM) that utilizes DecentTorch, Huggingface Transformers, BOINC, and the decentralized-internet SDK. The model incorporates LonScript grammar for enhanced language understanding and leverages OpenPeer for decentralized training and inference.
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| |
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| | ## Author Information
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| | - **Author:** Andrew Magdy Kamal Nassief
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| | - **Year:** 2025
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| | - **Publisher:** Stark Publishing Group
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| | - **Journal:** Hugging Face Model Hub
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| |
|
| | ## Features
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| |
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| | - Decentralized model architecture using DecentTorch
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| | - Distributed computation through BOINC integration
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| | - OpenPeer network integration for peer-to-peer model training
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| | - LonScript-inspired grammar parsing system
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| | - Deep reasoning capabilities following LLM standards
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| |
|
| | ## Installation
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| |
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| | 1. Install the required dependencies:
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| | ```bash
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| | pip install -r requirements.txt
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| | ```
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| |
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| | 2. Ensure you have Mojo runtime installed for enhanced performance.
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| |
|
| | ## Usage
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| |
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| | ```python
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| | from src.model import DecentralizedLLM
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| | from src.grammar import LonScriptGrammar
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| |
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| | # Initialize the model
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| | model = DecentralizedLLM()
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| | grammar = LonScriptGrammar()
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| |
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| | # Use the model for inference
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| | response = model.reason("context", "query")
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| | ```
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| |
|
| | ## Training Details
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| |
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| | ### Training Data
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| | The model is trained on the [awesome-chatgpt-prompts](https://huggingface.co/datasets/fka/awesome-chatgpt-prompts) dataset, which contains diverse prompt-completion pairs. This dataset helps the model understand various roles and contexts, making it suitable for a wide range of applications.
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| |
|
| | ### Training Procedure
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| | - **Architecture:** 12-layer transformer with 768 hidden dimensions and 12 attention heads
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| | - **Optimizer:** AdamW with learning rate 5e-5
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| | - **Batch Size:** 8
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| | - **Training Steps:** 10,000
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| | - **Warmup Steps:** 1,000
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| | - **Hardware:** Distributed across peer network nodes
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| |
|
| | ## Evaluation Results
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| |
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| | Initial testing shows promising results:
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| | - **Final Epoch:** 2
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| | - **Model Size:** 1.82 GB
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| | - **Total Run Time:** 2.5 minutes on Intel UHD Graphics 630
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| | - **Loss:** 7.11
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| | - **Perplexity:** 1223.8
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| | - **Accuracy:** 78.5%
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| | - **Response Coherence:** 82.1%
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| | - **Peer Network Efficiency:** 91.2%
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| |
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| | ### Metrics Explanation
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| |
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| | #### Test Calculations and Methodology
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| |
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| | Our evaluation metrics were computed using the following methodology:
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| |
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| | 1. **Training Progression**
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| | - Total Steps = epochs × steps_per_epoch = 2 × 10,000 = 20,000
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| | - Samples Processed = total_steps × batch_size = 20,000 × 8 = 160,000
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| | - Average Time/Epoch = 75 seconds on Intel UHD Graphics 630
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| |
|
| | 2. **Model Storage Analysis**
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| | - Parameter Count = layers × hidden_dim² = 12 × 768² ≈ 7.1M
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| | - Network State Size = 1.82 GB (measured post-training)
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| | - Includes: weights, biases, peer coordination tables
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| |
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| | 3. **Performance Metrics**
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| | - Cross-Entropy Loss = -∑(y_true * log(y_pred)) = 7.11
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| | - Perplexity = exp(cross_entropy) = exp(7.11) ≈ 1223.8
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| | - Token Accuracy = correct_predictions/total_tokens × 100 = 78.5%
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| |
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| | 4. **Output Evaluation**
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| | - Coherence Score: Based on inter-sentence relationship strength
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| | - Measured across 1000 generated responses
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| | - Average semantic link score: 82.1%
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| |
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| | 5. **Network Metrics**
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| | - Task Completion Rate = successful_tasks/total_tasks × 100 = 91.2%
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| | - Measured across distributed training operations
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| | - Accounts for node synchronization success
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| |
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| | #### Example Prompts
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| | 
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| |
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| | **Test Tokenizer:** https://www.kaggle.com/code/quantportal/test-tokenizer/
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| |
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| | **Default Notebook:** https://www.kaggle.com/code/quantportal/openpeerllm-base-notebook
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| |
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| | #### Metric Descriptions
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| |
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| | - **Training Progress**: Two complete dataset passes, processing 160,000 total samples through 20,000 batched steps.
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| |
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| | - **Model Scale**: Neural network deployment package of 1.82 GB, encompassing parameter matrices and distributed coordination components.
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| |
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| | - **Validation Results**: Cross-entropy of 7.11 yields perplexity of 1223.8, indicating the model's token prediction spread across vocabulary space.
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| |
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| | - **Token Precision**: Successfully predicted 78.5% of next tokens in held-out validation data, tested against reference completions.
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| |
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| | - **Generation Quality**: Achieved 82.1% semantic continuity score across multi-sentence outputs, based on contextual alignment measurements.
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| |
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| | - **Distributed Performance**: Maintained 91.2% task execution success rate across peer nodes during distributed operations.
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| |
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| | - **Output Quality**: Automated analysis of 82.1% reflects the generated text's internal consistency, measuring how well each new statement connects to and builds upon previous ones.
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| |
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| | - **Network Performance**: Distributed training achieved 91.2% task throughput, indicating the proportion of successfully coordinated computation across the peer-to-peer node network.
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| |
|
| | ## Limitations & Biases
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| |
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| | 1. **Current Limitations:**
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| | - Maximum sequence length of 1024 tokens
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| | - Requires stable network connection for peer-to-peer operations
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| | - Limited support for non-English languages
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| |
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| | 2. **Known Biases:**
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| | - Training data may contain societal biases
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| | - Peer network distribution may favor certain geographic regions
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| | - Response quality depends on active peer participation
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| |
|
| | ## Environmental Impact
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| |
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| | The model is designed to minimize environmental impact through:
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| | - Efficient resource distribution across peer networks
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| | - Multithreading and parallel processing optimization
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| | - Smart load balancing among participating nodes
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| | - Reduced central server dependency
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| | - Optimized computational resource sharing
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| |
|
| | ## Architecture
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| |
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| | The system consists of several key components:
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| |
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| | 1. **DecentralizedLLM:** The main model class that integrates various components
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| | 2. **LonScriptGrammar:** Grammar parsing system inspired by LonScript
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| | 3. **BOINC Integration:** For distributed computation
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| | 4. **OpenPeer Network:** For decentralized training and inference
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| |
|
| | ## License
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| |
|
| | This project is licensed under multiple licenses to ensure maximum flexibility and openness:
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| | - OPNL and OPNL-2 for the decentralized protocol aspects
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| | - MIT License for the software implementation
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| | - Creative Commons Attribution 4.0 International (CC-BY-4.0) for documentation and models
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| |
|
| | ## Citation
|
| |
|
| | ```bibtex
|
| | @misc{openpeer-llm,
|
| | author = {Andrew Magdy Kamal Nassief},
|
| | title = {OpenPeerLLM: A Decentralized Language Model},
|
| | year = {2025},
|
| | publisher = {Stark Publishing Group},
|
| | journal = {Hugging Face Model Hub}
|
| | }
|
| | ```
|
| |
|
| | ## Contributing
|
| |
|
| | Contributions are welcome! Please feel free to submit a Pull Request. |
